Research Assistant in 3D Computer Vision and Geometric Deep Learning South Kensington, England
Job description
An exciting opportunity has arisen for a Research Assistant to work under the direction of Dr Stefanos Zafeiriou and Dr. Tolga Birdal.
Imperial College is consistently in the top 10-world university ranking. The College achieved second place in the Complete University Guide’s Computer Science subject table for 2017. The Department of Computing has repeatedly been awarded the highest research ratings in the REF assessments, was ranked 1st in the Research Intensity table published by The Times Higher, and was rated as "Excellent" in the most recent national assessment of teaching quality.
Duties and responsibilities
The main aim of the project is to develop next-generation machine learning and computer vision methods for analysis and synthesis of 3D textured data (bodies, hands, faces) using novel non-Euclidean Machine Learning techniques. The project will also involve data collection with state-of-the-art systems for body and face capture.
Within the project, the Research Assistant will be responsible for the development of effective and efficient machine learning algorithms for deep learning on graph, focusing on both generative and discriminative models GAN. The applicant is expected to have strong publication record in top conferences (CVPR, ICCV, ECCV, ICML, and alike) and journal papers (TPAMI, IJCV, TAC, TIP, and other high-impact journals).
Essential requirements
A MSc degree in a computing or related field, or equivalent
A track record of publications in excellent venues such as (CVPR, ICCV, ECCV, ICML, TPAMI, etc.)
A strong background in maths
Knowledge in one or more of the following areas: machine learning, computer vision, geometry processing.- Good knowledge of deep learning (including frameworks such as PyTorch or TensorFlow), and in particular deep learning on graphs
Please see job description for a full list of requirements.
Further information
In addition to completing the online application candidate should attach.
- A full CV
For queries regarding the application process contact Jamie Perrins: [email protected]
Documents
- Research Assistant JD.pdf
